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Record W2049604252 · doi:10.1145/2735386.2735922

Feature modelling and traceability for concern-driven software development with TouchCORE

2015· article· en· W2049604252 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceTraceabilityModularity (biology)Software engineeringSoftwareVisualizationSoftware developmentFeature (linguistics)Feature modelTracingData scienceHuman–computer interactionArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

This demonstration paper presents TouchCORE, a multi-touch enabled software design modelling tool aimed at developing scalable and reusable software design models following the concerndriven software development paradigm. After a quick review of concern-orientation, this paper primarily focusses on the new features that were added to TouchCORE since the last demonstration at Modularity 2014 (were the tool was still called TouchRAM). TouchCORE now provides full support for concern-orientation. This includes support for feature model editing and different modes for feature model and impact model visualization and assessment to best assist the concern designers as well as the concern users. To help the modeller understand the interactions between concerns, TouchCORE now also collects tracing information when concerns are reused and stores that information with the woven models. This makes it possible to visualize from which concern(s) a model element in the woven model has originated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.042
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.300
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it